Clean Water Issues, Community Behavior and Communication Models in Sustainable Development Goals 6 in Banten West Java Indonesia
Bibliographic record
Abstract
Banten Province has four regencies and four cities, the city with the highest Regional Original Income is Tangerang City and the lowest is Serang City.The total population is 11.904.562people with the densest population occupied by Tangerang City as many as 1.895.486people while the area with the smallest population is Cilegon City with 434.896 people.The purpose of this study is to describe the data on the achievement of SDGs 6 in the Banten region as well as to find out the problems that occur in the implementation of the program.Qualitative method used in this research with the aim of integrating secondary data with qualitative data in order to produce a comprehensive picture.Secondary data was obtained from the report of the Indonesian Central Statistics Agency in 2022, while qualitative data was obtained through interviews and observations.The results of this study indicate that there are some differences in the achievement of SDGs 6; the problem of disparity in infrastructure development for SDGs 6 which causes infrastructure inequality in accessing clean water and implementing healthy environmental sanitation; economic inequality/poverty; problems of education and public knowledge are still weak and have an impact on the weak literacy of SDGs 6 as well; the weakness of community PHBS which is difficult to change.The participatory development communication model is a solution to the weak participation of stakeholders in achieving SDGS 6 in Banten Province.The recommendation resulting from this research to build synergy between various sectors.The implication of this research is the need for new policies to be made to foster public awareness about healthy lifestyles, policies on socialization, dissemination of information and management innovations, and environmental sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".